Abstract
Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and straightforward theory. The usual distinction between factor analysis as a falsifiable model, and component analysis as a tautology, is shown to be misleading, since a special case of regression component decomposition can be defined which is not only falsifiable, but empirically indistinguishable from the factor model.
Keywords
Component (thermodynamics)Tautology (logic)MathematicsComponent analysisRegression analysisClass (philosophy)FalsifiabilityRegressionLatent class modelFactor (programming language)Range (aeronautics)StatisticsEconometricsComputer scienceArtificial intelligence
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Publication Info
- Year
- 1976
- Type
- article
- Volume
- 29
- Issue
- 2
- Pages
- 175-189
- Citations
- 86
- Access
- Closed
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Cite This
Peter H. Schönemann,
James H. Steiger
(1976).
REGRESSION COMPONENT ANALYSIS.
British Journal of Mathematical and Statistical Psychology
, 29
(2)
, 175-189.
https://doi.org/10.1111/j.2044-8317.1976.tb00713.x
Identifiers
- DOI
- 10.1111/j.2044-8317.1976.tb00713.x